Description
The talk examines power noise modelling through Gaussian Processes for secure True Random Number Generators.
While revisiting one-sided fractional Brownian motion, we obtain novel contributions by quantifying posterior uncertainty in exact analytical form, establishing quasi-stationary properties, and developing rigorous time-frequency analysis. These results are applied to model oscillator fluctuations of power-noise type, enabling closed-form entropy expressions for TRNGs and a novel GPU-accelerated simulation technique valuable for studying non-standard post-processing.
This work bridges machine learning techniques and signal processing to solve hardware security applications.
Keywords
Gaussian Process, Power Noise, True Random Number Generator, Fractional Brownian Motion, Entropy Estimation, Hardware Security, GPU Acceleration
Practical infos
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Cryptanalysis of full BEANIE
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BEANIE is a tweakable block cipher recently published at ToSC aiming for memory encryption of microcontroller units. In line with this goal, it handles small plaintexts of only 32 bits and has a low latency. In this paper, we propose the first third-party analysis of the two variants of BEANIE. By carefully leveraging structural properties of the cipher and taking advantage of its distinctive[…]-
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